Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

Context. Large aperture ground-based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, ground-based observations are inherently limited due to Earth’s turbulent atmosphere, requiring image correction techniques.

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Author Schirninger, C.
Jarolim, Robert
Veronig, A. M.
Kuckein, C.
Publisher UCAR/NCAR - Library
Publication Date 2025-01-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T19:55:11.307237
Metadata Record Identifier edu.ucar.opensky::articles:42530
Metadata Language eng; USA
Suggested Citation Schirninger, C., Jarolim, Robert, Veronig, A. M., Kuckein, C.. (2025). Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7m61qkg. Accessed 12 August 2025.

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